A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread

TL;DR

This paper introduces a machine learning-enhanced Covid-19 diagnostic model based on an augmented SIR framework, capable of analyzing and comparing quarantine policies' effectiveness across regions using publicly available data.

Contribution

It presents a novel neural network-augmented SIR model that does not depend on prior epidemics, enabling global quarantine policy assessment using machine learning.

Findings

Strong correlation between quarantine measures and infection control across regions

Model effectively decomposes infection data to analyze policy impacts

Publicly hosts quarantine diagnosis results for 70 countries

Abstract

We have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon previous epidemics like SARS/MERS and all parameters are optimized via machine learning algorithms employed on publicly available Covid-19 data. The model decomposes the contributions to the infection timeseries to analyze and compare the role of quarantine control policies employed in highly affected regions of Europe, North America, South America and Asia in controlling the spread of the virus. For all continents considered, our results show a generally strong correlation between strengthening of the quarantine controls as learnt by the model and actions taken by the regions' respective governments. Finally, we have hosted our quarantine diagnosis results for the top 70 affected countries worldwide, on a public platform, which can be used for informed decision making by public health officials and researchers alike.